10:30-12:00 on Wednesday, Second Semester
Lecture Room #3, General Research Building #7
This course is jointly taught by Prof. Toyoaki Nishida and Prof. Yoshimasa Ohmoto.
Overview
Conversational interaction is considered to be a powerful communication means for intelligent actors, either natural or artificial, to interact each other to act as a collective intelligence. In this course, we study the mechanism of conversational interactions with verbal and nonverbal cues from computational points of view and discuss key issues in designing conversational systems that can interact with people in a conversational fashion.
Agenda (planned)
- Introduction (October 5) Nishida
Slides - History of Conversational Systems (October 12) Nishida
Slides - Methodologies for Conversational System Development (October 19, 26) Nishida
slides - Affective Computing (November 2) Nishida
slides - Theory of Mind (December 7) Nishida
slides - Smart Conversation Space (November 9) Ohmoto
slides - Measurement, Analysis and Modeling (November 16) Ohmoto
slides - Cognitive Design (November 30) Ohmoto
slides - Learning by Imitation (December 14) Nishida
slides - Aspects of Conversation -1- (December 21) Nishida
slides - Aspects of Conversation -2- (December 28) Nishida
(the first half of) slides - Aspects of Conversation -3- (January 11) Nishida
(the second half of) slides - Storytelling, Games, and Conversation; Synergy and Wrap up (January 18) Nishida
slides
Course materials
- Textbook :
Toyoaki Nishida, Atsushi Nakazawa, Yoshimasa Ohmoto, Yasser Mohammad. Conversational Informatics―Data Intensive Approach with Emphasis on Nonverbal Communication, Springer 2014.
http://link.springer.com/book/10.1007%2F978-4-431-55040-2 - Reading:
Yasser Mohammad and Toyoaki Nishida. Data Mining for Social Robotics – Toward Autonomously Social Robots, Springer 2015.
http://www.springer.com/us/book/9783319252308 - Additional materials will be provided by lecturers.
Credits
Will be awarded based on a report on subjects given at the class. Due date (January 31st, 2017)
Take-Home Knowledge
- Students will develop fundamental knowledge, including the history of the field and potential applications, for learning more advanced subjects on human-agent interaction.
- Students will obtain minimal skill for conducting experiment to take an empirical approach to human-agent interaction.